




"""simple docstring"""




import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel



def        a__					(							snake_case__			,					snake_case__=False							)		->   Union[str, Any]:
			lowerCamelCase      =   OmegaConf.load(snake_case__							)
			if display:
						print(yaml.dump(OmegaConf.to_container(snake_case__							)							)							)
			return config



def        a__					(							snake_case__			,					snake_case__=None			,					snake_case__=None							)		->   Tuple:
			if conf_path is None:
						lowerCamelCase      =   """./model_checkpoints/vqgan_only.yaml"""
			lowerCamelCase      =   load_config(snake_case__			,					display=snake_case__							)
			lowerCamelCase      =   VQModel(**config.model.params							)
			if ckpt_path is None:
						lowerCamelCase      =   """./model_checkpoints/vqgan_only.pt"""
			lowerCamelCase      =   torch.load(snake_case__			,					map_location=snake_case__							)
			if ".ckpt" in ckpt_path:
						lowerCamelCase      =   sd["""state_dict"""]
			model.load_state_dict(snake_case__			,					strict=snake_case__							)
			model.to(snake_case__							)
			del sd
			return model



def        a__					(							snake_case__			,					snake_case__							)		->   Dict:
			lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase      =   model.encode(snake_case__							)
			print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'							)
			lowerCamelCase      =   model.decode(snake_case__							)
			return xrec



def        a__					(							snake_case__			,					snake_case__=False							)		->   str:
			lowerCamelCase ,  lowerCamelCase      =   string.rsplit("""."""			,					1							)
			if reload:
						lowerCamelCase      =   importlib.import_module(snake_case__							)
						importlib.reload(snake_case__							)
			return getattr(importlib.import_module(snake_case__			,					package=snake_case__							)			,					cls							)



def        a__					(							snake_case__							)		->   List[Any]:
			if "target" not in config:
						raise KeyError("""Expected key `target` to instantiate."""							)
			return get_obj_from_str(config["""target"""]							)(**config.get("""params"""			,					{}							)							)



def        a__					(							snake_case__			,					snake_case__			,					snake_case__=True			,					snake_case__=True							)		->   Union[str, Any]:
			lowerCamelCase      =   instantiate_from_config(snake_case__							)
			if sd is not None:
						model.load_state_dict(snake_case__							)
			if gpu:
						model.cuda()
			if eval_mode:
						model.eval()
			return {"model": model}



def        a__					(							snake_case__			,					snake_case__			,					snake_case__			,					snake_case__							)		->   Any:
			# load the specified checkpoint
			if ckpt:
						lowerCamelCase      =   torch.load(snake_case__			,					map_location="""cpu"""							)
						lowerCamelCase      =   pl_sd["""global_step"""]
						print(F'loaded model from global step {global_step}.'							)
			else:
						lowerCamelCase      =   {"""state_dict""": None}
						lowerCamelCase      =   None
			lowerCamelCase      =   load_model_from_config(config.model			,					pl_sd["""state_dict"""]			,					gpu=snake_case__			,					eval_mode=snake_case__							)["""model"""]
			return model, global_step









"""simple docstring"""




from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map



def        a__					(							snake_case__							)		->   Dict[str, torch.Tensor]:
			lowerCamelCase      =   []
			lowerCamelCase      =   []
			lowerCamelCase      =   []

			for rt in rc.restypes:
						lowerCamelCase      =   rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
						restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]							)
						lowerCamelCase      =   {name: i for i, name in enumerate(snake_case__							)}
						restype_atomaa_to_atomaa_list.append(
						    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]							)

						restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]							)

			# Add dummy mapping for restype 'UNK'
			restype_atomaa_to_atomaa_list.append([0] * 14							)
			restype_atomaa_to_atomaa_list.append([0] * 37							)
			restype_atomaa_mask_list.append([0.0] * 14							)

			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.intaa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.intaa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.floataa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   protein["""aatype"""].to(torch.long							)

			# create the mapping for (residx, atom14) --> atom37, i.e. an array
			# with shape (num_res, 14) containing the atom37 indices for this protein
			lowerCamelCase      =   restype_atomaa_to_atomaa[protein_aatype]
			lowerCamelCase      =   restype_atomaa_mask[protein_aatype]

			lowerCamelCase      =   residx_atomaa_mask
			lowerCamelCase      =   residx_atomaa_to_atomaa.long()

			# create the gather indices for mapping back
			lowerCamelCase      =   restype_atomaa_to_atomaa[protein_aatype]
			lowerCamelCase      =   residx_atomaa_to_atomaa.long()

			# create the corresponding mask
			lowerCamelCase      =   torch.zeros([21, 37]			,					dtype=torch.floataa			,					device=protein["""aatype"""].device							)
			for restype, restype_letter in enumerate(rc.restypes							):
						lowerCamelCase      =   rc.restype_atoa[restype_letter]
						lowerCamelCase      =   rc.residue_atoms[restype_name]
						for atom_name in atom_names:
									lowerCamelCase      =   rc.atom_order[atom_name]
									lowerCamelCase      =   1

			lowerCamelCase      =   restype_atomaa_mask[protein_aatype]
			lowerCamelCase      =   residx_atomaa_mask

			return protein



def        a__					(							snake_case__							)		->   Dict[str, np.ndarray]:
			lowerCamelCase      =   tree_map(lambda snake_case__							: torch.tensor(snake_case__			,					device=batch["""aatype"""].device							)			,					snake_case__			,					np.ndarray							)
			lowerCamelCase      =   tensor_tree_map(lambda snake_case__							: np.array(snake_case__							)			,					make_atomaa_masks(snake_case__							)							)
			return out



